AI is reshaping how we work, create, and make decisions, much like the internet did decades ago.
From automating tasks to generating code and analyzing data, employees across industries are turning to AI tools to work faster and smarter.
However, most organizations have no visibility into how, where, or why it’s being used.
Security researcher in the Threat Intelligence unit for LevelBlue.
This unsanctioned use of AI tools by employees without the approval or monitoring of an organization’s IT management is known as Shadow AI.
Blind trust in AI outputs, poor cybersecurity training, and a lack of clear governance are allowing sensitive data, intellectual property, and even decision-making processes to slip beyond organizational control. What began as a tool for efficiency is also fueling unseen threats.
In this piece, I’ll explore what’s driving the rise of Shadow AI, the risks it poses to businesses, and what organizations can do to regain visibility and control before the problem grows larger.
What’s Driving the Shadow AI Surge
The rapid rise of Shadow AI isn’t the result of malicious intent, but rather a lack of awareness and education. Employees who use AI in their personal lives often bring those same tools into the workplace, assuming they’re safe for company-approved systems. For many, the line between personal and professional use has blurred.
With the rapid advancement of AI tools, some organizations have yet to establish clear policies or training guidelines on what constitutes appropriate AI use in the workplace.
Without explicit guidance, employees experiment on their own. Likewise, the convenience and popularity of AI tools often outweigh perceived risk. In many ways, it mirrors the early days of Shadow IT, where employees once turned to unapproved SaaS tools and messaging apps to accelerate productivity.
Only this time, the stakes are far higher because, unlike Shadow IT, Shadow AI doesn’t just move data around; it transforms, exposes, and learns from data, introducing unseen vulnerabilities and higher risks.
Key Risks of Shadow AI
Unmanaged AI adoption introduces a range of risks. The most immediate concern is data leakage, a problem highlighted by this year’s DeepSeek breach. When employees feed confidential information into public AI tools, that data may be logged, retained, or even used to train future models. This can lead to violations of data protection laws such as GDPR or HIPAA, and in some cases, even data espionage.
The storage of sensitive information on servers located in unaffiliated countries raises concerns about potential data theft or geopolitical surveillance. For that reason, several government agencies across the U.S. and Europe have banned the use of DeepSeek within their organizations.
Another major risk is legal and regulatory liabilities. When employees rely on AI-generated outputs without validating their accuracy or legality, they open the door to serious consequences from copyright violations and privacy breaches to full-scale compliance failures.
Unauthorized sharing of personal or sensitive information with external models can also trigger breach notifications, regulatory investigations, and contractual violations, exposing the organization to costly fines and reputational damage.
These risks are being amplified by emerging trends such as vibe coding and agentic AI. In some cases, code is deployed directly into production without review.
These flaws can remain hidden until exploited. Agentic AI poses an even broader concern. Internal AI agents, built to automate workflows or assist employees, are often granted overly permissive access to organizational data.
Without strict controls, they can become a backdoor to sensitive systems, exposing confidential records or triggering unintended actions. Together, these practices expand the attack surface in ways that evolve faster than most organizations can detect or contain.
Equally concerning is blind trust in AI outputs. As users grow more comfortable with these systems, their level of scrutiny drops. Inaccurate or biased results can spread unchecked through workflows, and when used outside sanctioned environments, IT teams lose the visibility needed to identify errors or investigate incidents.
Why Visibility Is Essential to Address and Mitigate the Shadow AI Threat
Addressing Shadow AI starts with visibility. Organizations can’t protect what they can’t see, and currently, many have little to no insight into how AI tools are being utilized across their networks.
The first step is to assess where AI is being used and to establish clear, updated policies that define what’s approved, what’s restricted, and under what conditions AI can be used. Enterprise-wide training is equally critical to help employees understand both the benefits and the risks of using large language models (LLMs).
Companies should also provide the right resources. When employees have access to sanctioned AI tools that meet their business needs, they’re far less likely to seek out unauthorized ones. Developers, for example, may require specialized models hosted in secure environments to safely leverage AI without exposing proprietary code or customer data.
Next, organizations must integrate AI into their security architecture. Privileged access management should be implemented for the access of certain LLMs, ensuring that only authorized users can interact with sensitive systems or datasets. Security teams should also deploy technical controls, such as CASB, DLP, or proxy filtering, to detect and block Shadow AI usage.
Ultimately, Shadow AI will not wait for your policy. It’s already shaping workflows, decisions, and data flows across organizations. The choice is not whether to allow AI, but whether to manage it.
Organizations that bring visibility, control, and accountability to their AI usage can enable innovation safely, but ignoring Shadow AI won’t make it go away. It’s far better to confront it head-on, understand how it’s being used, and manage the risks before they manage you.
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